r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows

Abstract

Abstract. r.avaflow represents an innovative open-source computational tool for routing rapid mass flows, avalanches, or process chains from a defined release area down an arbitrary topography to a deposition area. In contrast to most existing computational tools, r.avaflow (i) employs a two-phase, interacting solid and fluid mixture model (Pudasaini, 2012); (ii) is suitable for modelling more or less complex process chains and interactions; (iii) explicitly considers both entrainment and stopping with deposition, i.e. the change of the basal topography; (iv) allows for the definition of multiple release masses, and/or hydrographs; and (v) serves with built-in functionalities for validation, parameter optimization, and sensitivity analysis. r.avaflow is freely available as a raster module of the GRASS GIS software, employing the programming languages Python and C along with the statistical software R. We exemplify the functionalities of r.avaflow by means of two sets of computational experiments: (1) generic process chains consisting in bulk mass and hydrograph release into a reservoir with entrainment of the dam and impact downstream; (2) the prehistoric Acheron rock avalanche, New Zealand. The simulation results are generally plausible for (1) and, after the optimization of two key parameters, reasonably in line with the corresponding observations for (2). However, we identify some potential to enhance the analytic and numerical concepts. Further, thorough parameter studies will be necessary in order to make r.avaflow fit for reliable forward simulations of possible future mass flow events.

Sensitivity analysis and calibration of a dynamic physically based slope stability model

“…Further performance indicators could be used for this task instead (e.g. Formetta et al, 2016;Mergili et al, 2017). However, for validating the results of physically based slope stability models, a performance indicator which is independent of a threshold (such as the AUC) can be misleading.…”

“…The coordinates of the point in the ROC plot where the FOS falls below 1.0 represent the correctly predicted fractions of observed landslides (true positives; TP) and non-landslides (true negatives; TN). The basic idea of the calibration procedure is to identify parameter value combinations which result in an optimum prediction of observed landslides and non-landslides, at a FOS threshold falling below 1.0, by minimizing the distance to the perfect classification (D2PC; Formetta et al, 2016;Mergili et al, 2017; The identification of "behavioural model runs" out of the 10 000 calibration runs is based on the following observations and assumptions:…”

DebrisInterMixing-2.3: a finite volume solver for three-dimensional debris-flow simulations with two calibration parameters – Part 2: Model validation with experiments

“…These surface patterns occurred both in the simulation and in the experiment (b and B on the right, more apparent in the corresponding video from which the screenshots are taken; see Logan and Iverson, 2013). dition. OpenFOAM offers partial-slip boundary conditions; however, the definition would only become meaningful together with real two-phase mass-flow models as in , Mergili et al (2017) or as developed by Wardle and Weller (2013) within our 3-D framework. The application of a no-slip boundary condition to the USGS flume experiment with a sand-gravel mixture (called SG mixture in the following) on a smooth bed does not fulfill this requirement and was only chosen in order to have a model setup which is comparable to the sand-gravel mixture with loam (called SGM mixture in the following).…”

“…The mixture of different materials leads to a complex rheological behavior that is still not well understood. Field observations of debris-flow behavior and rheology are challenging and still rare, and numerical modeling is often the approach of choice when assessment of debris-flow behavior is needed for planning, zoning and hazard assessment (Scheuner et al, 2011;Christen et al, 2012;Kattel et al, 2016;Mergili et al, 2017). Most models require direct calibration to capture the site-specific behavior.…”

“…The composition is predefined, and no dynamic changes of the gravel concentration or of the share of fine material suspension are modeled at this stage. For the dynamic evolution of the solid and fluid concentrations and a separate treatment of velocities per phase, we refer to more general models and simulations Mergili et al, 2017).…”